diff --git a/rl_game/get_up/__init__.py b/rl_game/get_up/__init__.py new file mode 100644 index 0000000..f9ad0fb --- /dev/null +++ b/rl_game/get_up/__init__.py @@ -0,0 +1,13 @@ +import gymnasium as gym + +# 导入你的配置 +from rl_game.demo.config.t1_env_cfg import T1EnvCfg + +# 注册环境到 Gymnasium +gym.register( + id="Isaac-T1-GetUp-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", # Isaac Lab 统一的强化学习环境入口 + kwargs={ + "cfg": T1EnvCfg(), + }, +) \ No newline at end of file diff --git a/rl_game/get_up/asset/t1/T1_locomotion_base.usd b/rl_game/get_up/asset/t1/T1_locomotion_base.usd new file mode 100644 index 0000000..9787643 Binary files /dev/null and b/rl_game/get_up/asset/t1/T1_locomotion_base.usd differ diff --git a/rl_game/get_up/asset/t1/T1_locomotion_physics_lab.usd b/rl_game/get_up/asset/t1/T1_locomotion_physics_lab.usd new file mode 100644 index 0000000..65e3052 Binary files /dev/null and b/rl_game/get_up/asset/t1/T1_locomotion_physics_lab.usd differ diff --git a/rl_game/get_up/config/ppo_cfg.yaml b/rl_game/get_up/config/ppo_cfg.yaml new file mode 100644 index 0000000..e7af3eb --- /dev/null +++ b/rl_game/get_up/config/ppo_cfg.yaml @@ -0,0 +1,60 @@ +params: + seed: 42 + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 1.0 + fixed_sigma: False + mlp: + units: [512, 256, 128] + activation: relu + d2rl: False + initializer: + name: default + + config: + name: T1_Walking + env_name: rlgym # Isaac Lab 包装器 + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: 8192 # 同时训练的机器人数量 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 5e-4 + lr_schedule: adaptive + kl_threshold: 0.01 + score_to_win: 20000 + max_epochs: 500000 + save_best_after: 50 + save_frequency: 100 + grad_norm: 1.0 + entropy_coef: 0.01 + truncate_grads: True + bounds_loss_coef: 0.01 + e_clip: 0.2 + horizon_length: 256 + minibatch_size: 65536 + mini_epochs: 4 + critic_coef: 1 + clip_value: True \ No newline at end of file diff --git a/rl_game/get_up/config/t1_env_cfg.py b/rl_game/get_up/config/t1_env_cfg.py new file mode 100644 index 0000000..c5e25fd --- /dev/null +++ b/rl_game/get_up/config/t1_env_cfg.py @@ -0,0 +1,320 @@ +import torch +import random +import numpy as np +import isaaclab.envs.mdp as mdp +from isaaclab.assets import ArticulationCfg +from isaaclab.envs import ManagerBasedRLEnvCfg, ManagerBasedRLEnv +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.envs.mdp import JointPositionActionCfg +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass +from rl_game.get_up.env.t1_env import T1SceneCfg + + +# ========================================== +# 1. 核心逻辑区：非线性加法引导与惩罚机制 +# ========================================== + +def smooth_additive_getup_reward( + env: ManagerBasedRLEnv, + min_head_height: float, + min_pelvis_height: float, + foot_sensor_cfg: SceneEntityCfg, + arm_sensor_cfg: SceneEntityCfg, +) -> torch.Tensor: + """ + 指数级纯加法平滑奖励 (完全修复版)： + 融合了头部高度防驼背，以及脚底受力防跪地。 + """ + head_idx, _ = env.scene["robot"].find_bodies("H2") + pelvis_idx, _ = env.scene["robot"].find_bodies("Trunk") + foot_indices, _ = env.scene["robot"].find_bodies(".*_foot_link") + + head_h = env.scene["robot"].data.body_state_w[:, head_idx[0], 2] + pelvis_pos = env.scene["robot"].data.body_state_w[:, pelvis_idx[0], :3] + pelvis_h = pelvis_pos[:, 2] + + foot_pos = env.scene["robot"].data.body_state_w[:, foot_indices, :3] + feet_center_xy = torch.mean(foot_pos[:, :, :2], dim=1) + pelvis_xy = pelvis_pos[:, :2] + + projected_gravity = env.scene["robot"].data.projected_gravity_b + + # --- 基础状态比率 --- + upright_ratio = torch.clamp(1.0 - torch.norm(projected_gravity[:, :2], dim=-1), min=0.0, max=1.0) + raw_height_ratio = torch.clamp(pelvis_h / min_pelvis_height, min=0.0, max=1.0) + + # 🌟 修复 1：把头部高度加回来，防驼背 + head_ratio = torch.clamp(head_h / min_head_height, min=0.0, max=1.0) + + # 指数级高度比例 + height_ratio_sq = torch.square(raw_height_ratio) + + # 1. 核心主线分：加入 head_ratio，促使它把上半身彻底挺直 + core_reward = (height_ratio_sq * 3.0) + (upright_ratio * 1.0) + (head_ratio * 1.0) + + # --- 辅助过渡分 --- + foot_sensor = env.scene.sensors.get(foot_sensor_cfg.name) + arm_sensor = env.scene.sensors.get(arm_sensor_cfg.name) + foot_force_z = torch.clamp(torch.sum(foot_sensor.data.net_forces_w[:, :, 2], dim=-1), min=0.0) + arm_force_z = torch.clamp(torch.sum(arm_sensor.data.net_forces_w[:, :, 2], dim=-1), min=0.0) + + feet_to_pelvis_dist = torch.norm(feet_center_xy - pelvis_xy, dim=-1) + tuck_legs_reward = torch.exp(-3.0 * feet_to_pelvis_dist) + + arm_force_capped = torch.clamp(arm_force_z, min=0.0, max=200.0) + arm_push_reward = arm_force_capped / 200.0 + + # 🌟 修复 2：把脚底受力加回来，只要脚底板踩实了就给分，引导它脱离跪姿 + foot_contact_reward = (foot_force_z > 10.0).float() + + ground_factor = torch.clamp(1.0 - raw_height_ratio, min=0.0, max=1.0) + + # 将脚部接触分加入辅助奖励中 + aux_reward = ground_factor * (tuck_legs_reward * 0.5 + arm_push_reward * 0.5) + (foot_contact_reward * 0.5) + + return core_reward + aux_reward + + +def ground_farming_timeout( + env: ManagerBasedRLEnv, + max_time: float, + height_threshold: float +) -> torch.Tensor: + pelvis_idx, _ = env.scene["robot"].find_bodies("Trunk") + pelvis_h = env.scene["robot"].data.body_state_w[:, pelvis_idx[0], 2] + episode_time = env.episode_length_buf * env.step_dt + return ((episode_time > max_time) & (pelvis_h < height_threshold)).bool() + + +def root_height_below_minimum(env: ManagerBasedRLEnv, minimum_height: float) -> torch.Tensor: + pelvis_idx, _ = env.scene["robot"].find_bodies("Trunk") + pelvis_h = env.scene["robot"].data.body_state_w[:, pelvis_idx[0], 2] + return (pelvis_h < minimum_height).bool() + + +def is_standing_still( + env: ManagerBasedRLEnv, + min_head_height: float, + min_pelvis_height: float, + max_angle_error: float, + standing_time: float, + velocity_threshold: float = 0.15 +) -> torch.Tensor: + head_idx, _ = env.scene["robot"].find_bodies("H2") + pelvis_idx, _ = env.scene["robot"].find_bodies("Trunk") + current_head_h = env.scene["robot"].data.body_state_w[:, head_idx[0], 2] + current_pelvis_h = env.scene["robot"].data.body_state_w[:, pelvis_idx[0], 2] + gravity_error = torch.norm(env.scene["robot"].data.projected_gravity_b[:, :2], dim=-1) + root_vel_norm = torch.norm(env.scene["robot"].data.root_lin_vel_w, dim=-1) + + is_stable_now = ( + (current_head_h > min_head_height) & + (current_pelvis_h > min_pelvis_height) & + (gravity_error < max_angle_error) & + (root_vel_norm < velocity_threshold) + ) + + if "stable_timer" not in env.extras: + env.extras["stable_timer"] = torch.zeros(env.num_envs, device=env.device) + dt = env.physics_dt * env.cfg.decimation + env.extras["stable_timer"] = torch.where(is_stable_now, env.extras["stable_timer"] + dt, + torch.zeros_like(env.extras["stable_timer"])) + return (env.extras["stable_timer"] > standing_time).bool() + + +def anti_flying_penalty( + env: ManagerBasedRLEnv, + foot_sensor_cfg: SceneEntityCfg, + arm_sensor_cfg: SceneEntityCfg, +) -> torch.Tensor: + pelvis_idx, _ = env.scene["robot"].find_bodies("Trunk") + pelvis_h = env.scene["robot"].data.body_state_w[:, pelvis_idx[0], 2] + + foot_sensor = env.scene.sensors.get(foot_sensor_cfg.name) + arm_sensor = env.scene.sensors.get(arm_sensor_cfg.name) + + foot_force_z = torch.clamp(torch.sum(foot_sensor.data.net_forces_w[:, :, 2], dim=-1), min=0.0) + arm_force_z = torch.clamp(torch.sum(arm_sensor.data.net_forces_w[:, :, 2], dim=-1), min=0.0) + + # 必须双手双脚都悬空 (< 10N)，才算是在天上飞 + is_flying = ((pelvis_h > 0.4) & (foot_force_z < 10.0) & (arm_force_z < 10.0)).float() + return is_flying + + +def base_ang_vel_penalty(env: ManagerBasedRLEnv) -> torch.Tensor: + ang_vel = env.scene["robot"].data.root_ang_vel_w + return torch.sum(torch.square(ang_vel), dim=-1) + + +def anti_kneeling_penalty(env: ManagerBasedRLEnv) -> torch.Tensor: + """ + 🛠️ 修复版防跪地机制： + 使用正则匹配小腿 "Shank.*"。一旦骨盆抬离地面试图站立时，严格惩罚小腿碰地！ + """ + shank_indices, _ = env.scene["robot"].find_bodies("Shank.*") + shank_z = env.scene["robot"].data.body_state_w[:, shank_indices, 2] + + # 判断是否有任何小腿刚体高度低于 0.15 米 + is_kneeling = torch.any(shank_z < 0.15, dim=-1).float() + + pelvis_idx, _ = env.scene["robot"].find_bodies("Trunk") + pelvis_h = env.scene["robot"].data.body_state_w[:, pelvis_idx[0], 2] + + return is_kneeling * (pelvis_h > 0.35).float() + + +# ========================================== +# 2. 配置类区 +# ========================================== + +T1_JOINT_NAMES = [ + 'AAHead_yaw', 'Head_pitch', + 'Left_Shoulder_Pitch', 'Left_Shoulder_Roll', 'Left_Elbow_Pitch', 'Left_Elbow_Yaw', + 'Right_Shoulder_Pitch', 'Right_Shoulder_Roll', 'Right_Elbow_Pitch', 'Right_Elbow_Yaw', + 'Waist', + 'Left_Hip_Pitch', 'Right_Hip_Pitch', 'Left_Hip_Roll', 'Right_Hip_Roll', + 'Left_Hip_Yaw', 'Right_Hip_Yaw', 'Left_Knee_Pitch', 'Right_Knee_Pitch', + 'Left_Ankle_Pitch', 'Right_Ankle_Pitch', 'Left_Ankle_Roll', 'Right_Ankle_Roll' +] + + +@configclass +class T1ObservationCfg: + @configclass + class PolicyCfg(ObsGroup): + concatenate_terms = True + base_lin_vel = ObsTerm(func=mdp.base_lin_vel) + base_ang_vel = ObsTerm(func=mdp.base_ang_vel) + projected_gravity = ObsTerm(func=mdp.projected_gravity) + joint_pos = ObsTerm(func=mdp.joint_pos_rel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=T1_JOINT_NAMES)}) + joint_vel = ObsTerm(func=mdp.joint_vel_rel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=T1_JOINT_NAMES)}) + actions = ObsTerm(func=mdp.last_action) + + policy = PolicyCfg() + + +@configclass +class T1EventCfg: + reset_robot_rotation = EventTerm( + func=mdp.reset_root_state_uniform, + params={ + "asset_cfg": SceneEntityCfg("robot"), + "pose_range": { + "roll": (-3.14, 3.14), + "pitch": (-3.14, 3.14), + "yaw": (-3.14, 3.14), + "x": (0.0, 0.0), + "y": (0.0, 0.0), + "z": (0.45, 0.65), + }, + "velocity_range": {}, + }, + mode="reset", + ) + + +@configclass +class T1ActionCfg: + arm_action = JointPositionActionCfg( + asset_name="robot", + joint_names=[ + 'Left_Shoulder_Pitch', 'Left_Shoulder_Roll', 'Left_Elbow_Pitch', 'Left_Elbow_Yaw', + 'Right_Shoulder_Pitch', 'Right_Shoulder_Roll', 'Right_Elbow_Pitch', 'Right_Elbow_Yaw' + ], + scale=0.8, use_default_offset=True + ) + torso_action = JointPositionActionCfg( + asset_name="robot", + joint_names=['Waist', 'AAHead_yaw', 'Head_pitch'], + scale=0.8, use_default_offset=True + ) + leg_action = JointPositionActionCfg( + asset_name="robot", + joint_names=[ + 'Left_Hip_Pitch', 'Right_Hip_Pitch', 'Left_Hip_Roll', 'Right_Hip_Roll', + 'Left_Hip_Yaw', 'Right_Hip_Yaw', 'Left_Knee_Pitch', 'Right_Knee_Pitch', + 'Left_Ankle_Pitch', 'Right_Ankle_Pitch', 'Left_Ankle_Roll', 'Right_Ankle_Roll' + ], + scale=0.8, use_default_offset=True + ) + + +@configclass +class T1GetUpRewardCfg: + smooth_getup = RewTerm( + func=smooth_additive_getup_reward, weight=15.0, + params={ + "min_head_height": 1.08, "min_pelvis_height": 0.72, + "foot_sensor_cfg": SceneEntityCfg("contact_sensor", body_names=[".*_foot_link"]), + "arm_sensor_cfg": SceneEntityCfg("contact_sensor", body_names=["A[LR][23]", ".*_hand_link"]) + } + ) + + anti_fly = RewTerm( + func=anti_flying_penalty, + weight=-5.0, + params={ + "foot_sensor_cfg": SceneEntityCfg("contact_sensor", body_names=[".*_foot_link"]), + "arm_sensor_cfg": SceneEntityCfg("contact_sensor", body_names=["A[LR][23]", ".*_hand_link"]) + } + ) + + # 🌟 新增：重罚小腿触地的跪姿 + anti_kneeling = RewTerm(func=anti_kneeling_penalty, weight=-3.0) + + base_ang_vel = RewTerm(func=base_ang_vel_penalty, weight=-0.05) + action_rate = RewTerm(func=mdp.action_rate_l2, weight=-0.05) + joint_vel = RewTerm(func=mdp.joint_vel_l2, weight=-0.01) + + # 🌟 新增：大大降低成功的门槛，诱使它敢于站立 + is_success_bonus = RewTerm( + func=is_standing_still, + weight=100.0, + params={ + "min_head_height": 1.05, + "min_pelvis_height": 0.75, + "max_angle_error": 0.4, # 允许一定的弯腰 + "standing_time": 0.2, # 仅需保持 0.1 秒即可拿走巨额赏金！ + "velocity_threshold": 0.8 # 允许身体轻微晃动 + } + ) + + +@configclass +class T1GetUpTerminationsCfg: + time_out = DoneTerm(func=mdp.time_out) + anti_farming = DoneTerm( + func=ground_farming_timeout, + params={"max_time": 5.0, "height_threshold": 0.3} + ) + base_height = DoneTerm(func=root_height_below_minimum, params={"minimum_height": -0.2}) + illegal_contact = DoneTerm( + func=mdp.illegal_contact, + params={"sensor_cfg": SceneEntityCfg("contact_sensor", body_names=["Trunk"]), "threshold": 5000.0} + ) + standing_success = DoneTerm( + func=is_standing_still, + params={ + "min_head_height": 1.05, "min_pelvis_height": 0.75, + "max_angle_error": 0.2, "standing_time": 0.5, "velocity_threshold": 0.2 + } + ) + + +@configclass +class T1EnvCfg(ManagerBasedRLEnvCfg): + scene = T1SceneCfg(num_envs=8192, env_spacing=2.5) + observations = T1ObservationCfg() + rewards = T1GetUpRewardCfg() + terminations = T1GetUpTerminationsCfg() + events = T1EventCfg() + actions = T1ActionCfg() + episode_length_s = 10.0 + decimation = 4 \ No newline at end of file diff --git a/rl_game/get_up/env/t1_env.py b/rl_game/get_up/env/t1_env.py new file mode 100644 index 0000000..87cbae3 --- /dev/null +++ b/rl_game/get_up/env/t1_env.py @@ -0,0 +1,81 @@ +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors import ContactSensorCfg +from isaaclab.utils import configclass +from isaaclab.actuators import ImplicitActuatorCfg +from isaaclab import sim as sim_utils + +import os + +_DEMO_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +T1_USD_PATH = os.path.join(_DEMO_DIR, "asset", "t1", "T1_locomotion_physics_lab.usd") + +@configclass +class T1SceneCfg(InteractiveSceneCfg): + """最终修正版：彻底解决 Unknown asset config type 报错""" + + # 1. 地面配置：直接在 spawn 内部定义材质 + ground = AssetBaseCfg( + prim_path="/World/ground", + spawn=sim_utils.GroundPlaneCfg( + physics_material=sim_utils.RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + restitution=0.3, + friction_combine_mode="average", + restitution_combine_mode="average", + ) + ), + ) + + # 2. 机器人配置 + robot = ArticulationCfg( + prim_path="{ENV_REGEX_NS}/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=T1_USD_PATH, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=10.0, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=True, + solver_position_iteration_count=8, + solver_velocity_iteration_count=4, + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.2), # 掉落高度 + joint_pos={".*": 0.0}, + ), + actuators={ + # 1. 核心承重关节：大腿、膝盖、腰部 + "heavy_joints": ImplicitActuatorCfg( + joint_names_expr=["L.*_Hip_.*", "R.*_Hip_.*", "L.*_Knee_.*", "R.*_Knee_.*", "Waist.*"], + effort_limit=800.0, + velocity_limit=15.0, + stiffness=600.0, + damping=30.0, + ), + # 2. 末端/轻型关节：手臂、脚踝、头部 + "light_joints": ImplicitActuatorCfg( + joint_names_expr=["L.*_Shoulder_.*", "R.*_Shoulder_.*", "L.*_Elbow_.*", "R.*_Elbow_.*", ".*Ankle.*", ".*Head.*"], + effort_limit=300.0, + velocity_limit=25.0, + stiffness=200.0, + damping=10.0, + ), + }, + ) + + contact_sensor = ContactSensorCfg( + prim_path="{ENV_REGEX_NS}/Robot/.*", + update_period=0.0, + history_length=3, + ) + + # 3. 光照配置 + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) diff --git a/rl_game/get_up/train.py b/rl_game/get_up/train.py new file mode 100644 index 0000000..f7e344b --- /dev/null +++ b/rl_game/get_up/train.py @@ -0,0 +1,101 @@ +import sys +import os +import argparse + +# 确保能找到项目根目录下的模块 +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +from isaaclab.app import AppLauncher + +# 1. 配置启动参数 +parser = argparse.ArgumentParser(description="Train T1 robot to Get-Up with RL-Games.") +parser.add_argument("--num_envs", type=int, default=8192, help="起身任务建议并行 4096 即可") +parser.add_argument("--task", type=str, default="Isaac-T1-GetUp-v0", help="任务 ID") +parser.add_argument("--seed", type=int, default=42, help="随机种子") +AppLauncher.add_app_launcher_args(parser) +args_cli = parser.parse_args() + +# 2. 启动仿真器（必须在导入其他 isaaclab 模块前） +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +import torch +import gymnasium as gym +import yaml +from isaaclab_rl.rl_games import RlGamesVecEnvWrapper +from rl_games.torch_runner import Runner +from rl_games.common import env_configurations, vecenv + +# 导入你刚刚修改好的配置类 +# 假设你的文件名是 t1_getup_cfg.py，类名是 T1EnvCfg +from config.t1_env_cfg import T1EnvCfg + +# 3. 注册环境 +gym.register( + id="Isaac-T1-GetUp-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "cfg": T1EnvCfg(), # 这里会加载你设置的随机旋转、时间惩罚等 + }, +) + + +def main(): + # --- 新增：处理 Retrain 参数 --- + # 你可以手动指定路径，或者在 argparse 里增加一个 --checkpoint 参数 + checkpoint_path = os.path.join(os.path.dirname(__file__), "logs/T1_GetUp/nn/T1_GetUp.pth") + # 检查模型文件是否存在 + should_retrain = os.path.exists(checkpoint_path) + + env = gym.make("Isaac-T1-GetUp-v0", num_envs=args_cli.num_envs) + + # 注意：rl_device 必须设置为 args_cli.device (通常是 'cuda:0') + wrapped_env = RlGamesVecEnvWrapper( + env, + rl_device=args_cli.device, + clip_obs=5.0, + clip_actions=1.0 + ) + + vecenv.register('as_is', lambda config_name, num_actors, **kwargs: wrapped_env) + + env_configurations.register('rlgym', { + 'vecenv_type': 'as_is', + 'env_creator': lambda **kwargs: wrapped_env + }) + + config_path = os.path.join(os.path.dirname(__file__), "config", "ppo_cfg.yaml") + with open(config_path, "r") as f: + rl_config = yaml.safe_load(f) + + # 设置日志和实验名称 + rl_game_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".")) + log_dir = os.path.join(rl_game_dir, "logs") + rl_config['params']['config']['train_dir'] = log_dir + rl_config['params']['config']['name'] = "T1_GetUp" + + # --- 关键修改：注入模型路径 --- + if should_retrain: + print(f"[INFO]: 检测到预训练模型，正在从 {checkpoint_path} 恢复训练...") + # rl_games 会读取 config 中的 load_path 进行续训 + rl_config['params']['config']['load_path'] = checkpoint_path + else: + print("[INFO]: 未找到预训练模型，将从零开始训练。") + + # 7. 运行训练 + runner = Runner() + runner.load(rl_config) + + runner.run({ + "train": True, + "play": False, + # 如果你想强制从某个 checkpoint 开始，也可以在这里传参 + "checkpoint": checkpoint_path if should_retrain else None, + "vec_env": wrapped_env + }) + + simulation_app.close() + + +if __name__ == "__main__": + main() \ No newline at end of file